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app-video.py
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app-video.py
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# import the necessary packages
import os
import sys
import requests
import ssl
from flask import Flask
from flask import request
from flask import jsonify
from flask import send_file
from app_utils import download
from app_utils import generate_random_filename
from app_utils import clean_me
from app_utils import clean_all
from app_utils import create_directory
from app_utils import get_model_bin
from app_utils import convertToJPG
from os import path
import torch
import fastai
from deoldify.visualize import *
from pathlib import Path
import traceback
torch.backends.cudnn.benchmark=True
os.environ['CUDA_VISIBLE_DEVICES']='0'
app = Flask(__name__)
# define a predict function as an endpoint
@app.route("/process", methods=["POST"])
def process_video():
input_path = generate_random_filename(upload_directory,"mp4")
output_path = os.path.join(results_video_directory, os.path.basename(input_path))
try:
url = request.json["source_url"]
render_factor = int(request.json["render_factor"])
video_path = video_colorizer.colorize_from_url(source_url=url, file_name=input_path, render_factor=render_factor)
callback = send_file(output_path, mimetype='application/octet-stream')
return callback, 200
except:
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([
input_path,
output_path
])
if __name__ == '__main__':
global upload_directory
global results_video_directory
global video_colorizer
upload_directory = '/data/upload/'
create_directory(upload_directory)
results_video_directory = '/data/video/result/'
create_directory(results_video_directory)
model_directory = '/data/models/'
create_directory(model_directory)
video_model_url = 'https://www.dropbox.com/s/336vn9y4qwyg9yz/ColorizeVideo_gen.pth?dl=0'
get_model_bin(video_model_url, os.path.join(model_directory, 'ColorizeVideo_gen.pth'))
video_colorizer = get_video_colorizer()
port = 5000
host = '0.0.0.0'
app.run(host=host, port=port, threaded=False)